Alterlab Molecular Dynamics
技能 已验证 活跃Run and analyze molecular dynamics simulations with OpenMM and MDAnalysis. Set up protein/small molecule systems, define force fields, run energy minimization and production MD, analyze trajectories (RMSD, RMSF, contact maps, free energy surfaces). For structural biology, drug binding, and biophysics. Part of the AlterLab Academic Skills suite.
To enable researchers to conduct and analyze molecular dynamics simulations for structural biology, drug binding, and biophysics research.
功能
- Set up protein/small molecule systems
- Define force fields and water models
- Run energy minimization
- Perform NVT and NPT equilibration
- Execute production MD simulations
- Analyze trajectories (RMSD, RMSF, contacts)
- Estimate free energy surfaces
使用场景
- Analyze protein stability and conformational changes
- Simulate drug binding modes and residence times
- Study protein-protein interactions and binding energetics
- Investigate membrane protein dynamics
非目标
- Performing ab initio quantum mechanical calculations
- Directly controlling hardware for simulations
- Providing a graphical user interface for simulation setup
Code Execution
- info:ValidationInput parameters for functions are documented in docstrings, but explicit schema validation libraries like Zod or Pydantic are not used for runtime parameter constraint checking.
Execution
- info:Pinned dependenciesInstallation instructions suggest Conda or Pip, which can pin versions, but explicit lockfiles for reproducibility are not bundled with the skill itself.
安装
npx skills add AlterLab-IEU/AlterLab-Academic-Skills通过 npx 运行 Vercel skills CLI(skills.sh)— 需要本地安装 Node.js,以及至少一个兼容 skills 的智能体(Claude Code、Cursor、Codex 等)。前提是仓库遵循 agentskills.io 格式。
质量评分
已验证类似扩展
Molecular Dynamics
99Run and analyze molecular dynamics simulations with OpenMM and MDAnalysis. Set up protein/small molecule systems, define force fields, run energy minimization and production MD, analyze trajectories (RMSD, RMSF, contact maps, free energy surfaces). For structural biology, drug binding, and biophysics.
RDKit Cheminformatics Toolkit
99Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom sanitization, specialized algorithms.
PyTDC (Therapeutics Data Commons)
99Therapeutics Data Commons. AI-ready drug discovery datasets (ADME, toxicity, DTI), benchmarks, scaffold splits, molecular oracles, for therapeutic ML and pharmacological prediction.
Molfeat
99Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.
Medchem
99Medicinal chemistry filters. Apply drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, complexity metrics, for compound prioritization and library filtering.
Deepchem
99Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.